Introduction to Technical Questions
"If I don’t write Python, I won't land a data analytics position at a big tech company."
"The only way to get promoted or hired as a senior analyst is to use machine learning and build complex models."
"I don't have a data background—it’s impossible for me to break into analytics at a top tech company."
"To get hired as a senior analyst, I basically need to become a data scientist."
There’s a lot of noise out there about what skills are actually required to land a data analytics role in big tech. We’ve cut through the noise and gone straight to the source.
We analyzed hundreds of job descriptions and spoke directly with data professionals at companies like Meta, Amazon, Google, and Uber. What we learned is clear:
You don’t need to be a data scientist. You need to be a business-savvy problem solver who knows how to use data to drive decisions.
Core technical skills
Let’s set the record straight.
Yes, some roles do require Python—but if your job description doesn’t, then mastering ML pipelines or building neural networks is not what’s going to get you the job.
Your job description is always your source of truth. Read it carefully and tailor your prep accordingly.
Here’s what consistently shows up across most analytics roles:
1. SQL: non-negotiable
This is the one skill that shows up in almost every data analytics job at big tech firms—from entry-level to senior roles.
For example, you should be comfortable with the following:
- Joins, subqueries, and CTEs
- Window functions and aggregations
- Writing clean, optimized, and readable queries
We have an entire SQL module, designed by senior analysts from companies like Meta and Google, to help you refresh the fundamentals, avoid common mistakes, and walk confidently into SQL interviews—with multiple mock interviews to practice under pressure.
2. Excel & Google Sheets: still used, still tested
You may be surprised, even in big tech, spreadsheet skills still matter, especially for quick analysis and visualization, stakeholder requests, and dashboarding.
In data analytics interviews at top tech companies, Excel or Google Sheets may not appear as frequently as SQL—but that doesn’t mean you can ignore them. These tools still show up in interviews, especially in live case questions or take-home case studies, where you're expected to analyze datasets quickly and effectively.
Being fluent in Excel/Google Sheets demonstrates that you can move fast, build scrappy solutions, and extract insights without needing a full data stack, just like analysts often do on the job.
We built a focused module, written by analysts using Excel and Sheets daily at big tech, to show you exactly what functions, formulas, and use cases you’ll be tested on, and how to translate them to your day-to-day work.
3. Data visualization & dashboarding: communicate clearly
Data storytelling isn’t about fancy charts or tools. It’s about clearly communicating insights and building dashboards that drive action.
In the data visualization & dashboarding module, we worked with experts who've built real dashboards that reduced costs, optimized funnels, and influenced executives, so you can learn the decision-making frameworks behind effective visualization and dashboard design. You'll use these frameworks both in interviews and on the job.
4. Data analysis process
The best analysts don’t just jump into a dataset—they think critically about how and why they’re analyzing it.
Interviewers want to know:
- How do you collect data, especially if no perfect dataset exists?
- How do you clean and prepare messy real-world data?
- How do you structure your approach to generate meaningful insights?
Our module on data analysis process, plus a full set of mock interviews, will walk you through real scenarios where you’ll be tested on your structure, logic, and ability to turn raw data into clear recommendations.
5. Statistical analysis: know the core, go deep if needed
No, you don’t need to be a statistician. But if you’re interviewing for roles that support product teams, experimentation, or marketing analysis, you’ll be expected to understand:
- Probability and distributions
- Hypothesis testing
- Regression
- Confidence intervals
- A/B testing concepts
In the statistics & experimentation module, We’ve broken this down into two tracks—fundamentals and optional advanced lessons. Built with input from analysts at experimentation-heavy teams, these lessons focus on the exact statistical concepts you’ll be asked about—and how to explain them clearly in interviews.
6. Python & R: only if the role requires it
Some analytics roles—for example, especially those with data science or engineering overlap—expect fluency in Python or R. Most do not.
If Python is listed in the job description, we’ve included a focused module that pulls from our full Data Science course to help you quickly brush up on the core libraries and concepts most relevant to analytics. If it’s not listed, don’t waste your time here.
One more reminder: always go back to the job description
Always let the job description guide your prep, not your assumptions about what a data analyst should know.
If SQL and Excel are listed, focus there. If Python or advanced stats are required, dive deeper there.
And remember: knowing something isn't enough. You need to explain it, apply it, and communicate it clearly, especially under pressure.
This course is specifically designed to help you with that.